Gut microbiota predicts body fat change following a low-energy diet: a PREVIEW intervention study

Background Low-energy diets (LEDs) comprise commercially formulated food products that provide between 800 and 1200 kcal/day (3.3–5 MJ/day) to aid body weight loss. Recent small-scale studies suggest that LEDs are associated with marked changes in the gut microbiota that may modify the effect of the LED on host metabolism and weight loss. We investigated how the gut microbiota changed during 8 weeks of total meal replacement LED and determined their associations with host response in a sub-analysis of 211 overweight adults with pre-diabetes participating in the large multicentre PREVIEW (PREVention of diabetes through lifestyle intervention and population studies In Europe and around the World) clinical trial. Methods Microbial community composition was analysed by Illumina sequencing of the hypervariable V3-V4 regions of the 16S ribosomal RNA (rRNA) gene. Butyrate production capacity was estimated by qPCR targeting the butyryl-CoA:acetate CoA-transferase gene. Bioinformatics and statistical analyses, such as comparison of alpha and beta diversity measures, correlative and differential abundances analysis, were undertaken on the 16S rRNA gene sequences of 211 paired (pre- and post-LED) samples as well as their integration with the clinical, biomedical and dietary datasets for predictive modelling. Results The overall composition of the gut microbiota changed markedly and consistently from pre- to post-LED (P = 0.001), along with increased richness and diversity (both P < 0.001). Following the intervention, the relative abundance of several genera previously associated with metabolic improvements (e.g., Akkermansia and Christensenellaceae R-7 group) was significantly increased (P < 0.001), while flagellated Pseudobutyrivibrio, acetogenic Blautia and Bifidobacterium spp. were decreased (all P < 0.001). Butyrate production capacity was reduced (P < 0.001). The changes in microbiota composition and predicted functions were significantly associated with body weight loss (P < 0.05). Baseline gut microbiota features were able to explain ~25% of variation in total body fat change (post–pre-LED). Conclusions The gut microbiota and individual taxa were significantly influenced by the LED intervention and correlated with changes in total body fat and body weight in individuals with overweight and pre-diabetes. Despite inter-individual variation, the baseline gut microbiota was a strong predictor of total body fat change during the energy restriction period. Trial registration The PREVIEW trial was prospectively registered at ClinicalTrials.gov (NCT01777893) on January 29, 2013. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01053-7.

Total bacterial density (expressed as the logarithmic value of 16S rRNA gene copies per g feces) remained unchanged (P=0.31), while significant increases and decreases in the absolute abundances of Akkermansia, Bifidobacterium, Blautia, Christensenellaceae R-7 group, and Pseudobutyrivibrio were observed after the LED (all P<0.001). The directions of the changes were consistent with the relative abundance data. The qPCR assays were performed in a subset of 139 participants based on sample availability as described in Methods.

Construction of co-occurrence networks of pre-and post-LED microbiota
Co-occurrence networks ( Figure S3) were constructed by hierarchical clustering based on significant correlations between bacterial genera (clusters function in the mare package with default settings).
Additional file 1: Figure S3 A B

Baseline correlation between gut microbiota and adiposity
We assessed baseline associations between adiposity and the relative abundances of genera that were significantly associated with weight loss during the LED by fitting a negative binomial regression model (CovariateTest function in the mare package) while controlling for demographic variables. Intervention site (Finland and New Zealand) was additionally controlled for considering its potential confounding effect for baseline data. The results were consistent with no significant association found between the genera and body weight or body fat mass (FDR-P>0.05) ( Figure S4). The correlation between beta diversity and BMI change was assessed by both Spearman's correlation ( Figure S5) and partial Spearman's test adjusting for demographic variables (p = 0.001, adjusted R-squared = -0.06).

Additional file 1: Figure S5
Figure S5. Intra-individual Bray-Curtis (beta diversity) significantly associated with change in BMI.

Contribution of bacterial genera to imputed KEGG functional pathways
We analyzed the contribution of bacterial genera to the two imputed KEGG pathways (Glycosaminoglycan degradation and Flagellar assembly) that were significantly affected by the LED and associated with changes in clinical measurements during the LED. A breakdown of genera contributing to KEGG pathways was obtained by PICRUSt2 where the -stratified option was specified within picrust2_pipeline.py. The

Validation of predictive models in Finnish participants
Given body fat was measured using different equipment by Finland and New Zealand, we validated the findings on prediction of host responses using baseline microbiota in the Finnish cohort only (N=151). The prediction models were generated and presented following the same method described in the main text.

Top important features selected by Random Forest regression in linear mixed-effects model
RF-based models are suitable for gut microbiota data as they allow complex interrelationships within predictors and between predictors and the outcome, which however render interpretation challenging [5]. To test potential linear relationships between the selected microbiota features and body fat change, linear regression models (CovariateTest function in the mare package) were applied to the 10 most important genera selected by RF (Additional file 2). Since microbiota features are highly correlated, this method allowed us to assess the linear relationship with %change in adiposity for each feature. In these models, we controlled for the same potential confounding variables as used in the random forests.
Among the genera both selected by RF and linearly associated with body fat change  . Correlation between change in body fat mass and 10 bacterial taxa selected by random forests (RF) as being most highly predictive of body fat change. The correlation was estimated by linear mixed-effects models adjusting for demographic variables. The bar graph on the right denotes the importance, based on the percentage increase in mean-squared error (MSE), of the 10 genera to the accuracy of the RF model.